A comparison of classification techniques for monitoring and mapping land cover and land use changes in the subtropical region of Thai Nguyen, Vietnam : a thesis presented in partial fulfilment of the requirements for the degree of Master of Environmental Management at Massey University, Palmerston North, New Zealand

Deriving land cover/land-use information from earth observation satellite data is one of the most common applications for environmental monitoring, evaluation and management. Many parametric and non-parametric classification algorithms have been developed and applied to such applications. This study looks at the classification accuracies of three algorithms for different spatial and spectral resolution data. The performance of Random Forest (RF) was compared to Maximum Likelihood (MLC) and Artificial Neural Network (ANN) algorithms for the separation of subtropical land cover/land-use categories using Sentinel-2 and Landsat 8 data. The overall, producers’ and users’ accuracies were derived from the confusion matrix, while local land use statistics were also collected to evaluate the accuracy of classified images. The accuracy assessment showed the RF algorithm regularly outperformed the MLC and ANN in both types of imagery data (>90%). This approach also exhibited potential in dealing with the challenge of separating similar man-made features such as urban/built-up and mining extraction classes. The ANN algorithm had the lowest accuracy among the three classification algorithms, while Landsat 8 imagery was most suitable for the classification of subtropical mixed and complex landscapes. As the RF algorithm demonstrated a robustness and potential for mapping subtropical land cover/land-use, this study chose it to monitor and map temporal land cover/land-use changes in Thai Nguyen, Vietnam between 2000 and 2016. The results of this temporal monitoring revealed that there were substantial changes in land cover/land use over the course of 16 years. Agricultural and forest land decreased, while urban and mining extraction land expanded significantly, and water increased slightly. Changes in land cover/land-use are strongly associated with geographic locations. The conversion of agriculture and forest into urban/builtup and mining extraction land was detected largely in the Thai Nguyen central city and southern regions. In addition, further GIS analysis revealed that approximately 69.6% (100.2km2) of new built-up areas had occurred within 2km of primary roads, and nearly 96% (137.6km2) of new built-up expansion was detected within a 5-km buffer of the main roads. This study also demonstrates the potential of multi-temporal Landsat data and the combination of remote sensing, GIS and R programming to provide a timely, accurate and economical means to map and analyse temporal changes for long-term local land use development planning. Keywords: Random forest; Land cover mapping; Remote Sensing; Vietna

Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches

Accurate inventories of grasslands are important for studies of carbon dynamics, biodiversity conservation and agricultural management. For regions with persistent cloud cover the use of multi-temporal synthetic aperture radar (SAR) data provides an attractive solution for generating up-to-date inventories of grasslands. This is even more appealing considering the data that will be available from upcoming missions such as Sentinel-1 and ALOS-2. In this study, the performance of three machine learning algorithms; Random Forests (RF), Support Vector Machines (SVM) and the relatively underused Extremely Randomised Trees (ERT) is evaluated for discriminating between grassland types over two large heterogeneous areas of Ireland using multi-temporal, multi-sensor radar and ancillary spatial datasets. A detailed accuracy assessment shows the efficacy of the three algorithms to classify different types of grasslands. Overall accuracies ≥ 88.7% (with kappa coefficient of 0.87) were achieved for the single frequency classifications and maximum accuracies of 97.9% (kappa coefficient of 0.98) for the combined frequency classifications. For most datasets, the ERT classifier outperforms SVM and RF

Forest cover estimation in Ireland using radar remote sensing: a comparative analysis of forest cover assessment methodologies

Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1–98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting

Benchmark of machine learning methods for classification of a Sentinel-2 image

Thanks to mainly ESA and USGS, a large bulk of free images of the Earth is readily available nowadays. One of the main goals of remote sensing is to label images according to a set of semantic categories, i.e. image classification. This is a very challenging issue since land cover of a specific class may present a large spatial and spectral variability and objects may appear at different scales and orientations. In this study, we report the results of benchmarking 9 machine learning algorithms tested for accuracy and speed in training and classification of land-cover classes in a Sentinel-2 dataset. The following machine learning methods (MLM) have been tested: linear discriminant analysis, k-nearest neighbour, random forests, support vector machines, multi layered perceptron, multi layered perceptron ensemble, ctree, boosting, logarithmic regression. The validation is carried out using a control dataset which consists of an independent classification in 11 land-cover classes of an area about 60 km2, obtained by manual visual interpretation of high resolution images (20 cm ground sampling distance) by experts. In this study five out of the eleven classes are used since the others have too few samples (pixels) for testing and validating subsets. The classes used are the following: (i) urban (ii) sowable areas (iii) water (iv) tree plantations (v) grasslands. Validation is carried out using three different approaches: (i) using pixels from the training dataset (train), (ii) using pixels from the training dataset and applying cross-validation with the k-fold method (kfold) and (iii) using all pixels from the control dataset. Five accuracy indices are calculated for the comparison between the values predicted with each model and control values over three sets of data: the training dataset (train), the whole control dataset (full) and with k-fold cross-validation (kfold) with ten folds. Results from validation of predictions of the whole dataset (full) show the random forests method with the highest values; kappa index ranging from 0.55 to 0.42 respectively with the most and least number pixels for training. The two neural networks (multi layered perceptron and its ensemble) and the support vector machines - with default radial basis function kernel - methods follow closely with comparable performanc